Technological advances in imaging optics, sensors, and camera processing capabilities in today's digital cameras have resulted in high definition images or photos (e.g., those with over 10 megapixels). High resolution images may pose a challenge to imaging solutions in terms of computational complexity. For example, the complexity of an original Retinex-based color enhancement process may be on the order of N squared (O(N2)), where N is a number of input pixels. To reduce the complexity of imaging solutions and enable real-time operations, an upsampling imaging solution was proposed. The upsampling imaging solution approach may downsample an image from an original resolution to a low resolution, solve for the solution on the low resolution image, and then upsample the low resolution solution to the original resolution.
Meanwhile, upsampling imaging solutions may be applied to other scenarios where a low resolution solution and a high resolution image are available. For example, a KINECT sensor may give a depth image with a resolution of 640×480, while its Red Green Blue (RGB) camera may be capable of producing RGB images with a resolution of 1280×1024. Hence, it may be desirable to utilize the high resolution RGB image to construct a depth image with resolution of 1280×1024. Traditional upsampling methods may interpolate a low resolution solution directly and ignoring the knowledge of a high resolution image to a large extent, thereby thus sacrificing visual quality.